Functional linkages between leaf traits and net photosynthetic rate: reconciling empirical and mechanistic models
Bibliographic record
Abstract
Summary We had two objectives: (i) to determine the generality of, and extend the applicability of, a previously reported empirical relationship between leaf‐level net photosynthetic rate (AM, nmol g−1 s−1), specific leaf area (SLA, m2 kg−1) and leaf nitrogen mass fraction (NM, mmol g−1); and (ii) to compare these empirical results with a mechanistic model of photosynthesis in order to provide a mechanistic justification for the empirical pattern. Our results were based on both literature and original data. There were a total of 160 and 87 data points for the leaf‐level and whole‐plant data, respectively. Our multiple regression for single leaves was ln(AM) = 0·66 + 0·71 ln(SLA) + 0·79 ln(NM), r2 = −0·80; only the intercept (0·11) differed for the whole‐plant data. These results are not significantly different from previously published relationships. We then converted the mechanistic model of Evans and Poorter, and a modified version which includes leaf lamina thickness (T) and leaf dry matter (tissue) concentration (CM), into directed acyclic graphs. We then derived reduced graphs that involved only T, CM, SLA, NM and AM. These were tested using structural equation modelling, with measured lamina thickness (T′) and leaf dry matter ratio (LDMR, g dry mass g−1 fresh mass) as indicators of T and CM. The original Evans–Poorter model was rejected, but the modified version fitted the structural relationships well. The same qualitative models also applied to the whole‐plant data, although the path coefficients sometimes differed. Using simulations, we show that the original Evans–Poorter model predicts a positive correlation between SLA and NM that maximizes AM. The data closely follow this predicted relationship. The correlation between the actual values of AM (standardized units) and the predicted values obtained from the modified Evans–Poorter model was 0·74 and increased to 0·82 once three outlier points were removed. These results provide a mechanistic explanation for the empirical trends relating leaf form and carbon fixation, and predict that SLA and leaf N must be quantitatively co‐ordinated to maximize C fixation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".